Atmospheric methane evolution the last 40 years

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Atmos. Chem. Phys., 16, 3099–3126, 2016 www.atmos-chem-phys.net/16/3099/2016/ doi:10.5194/acp-16-3099-2016

Atmospheric methane evolution the last 40 years

Stig B. Dalsøren1, Cathrine L. Myhre2, Gunnar Myhre1, Angel J. Gomez-Pelaez3, Ole A. Søvde1, Ivar S. A. Isaksen1,4, Ray F. Weiss5, and Christina M. Harth5

1_{CICERO – Center for International Climate and Environmental Research Oslo, Oslo, Norway} 2_{NILU – Norwegian Institute for Air Research, Kjeller, Norway}

3_{Izaña Atmospheric Research Center (IARC), Meteorological State Agency of Spain (AEMET), Izaña, Spain} 4_{University of Oslo, Department of Geosciences, Oslo, Norway}

5_{Scripps Institution of Oceanography University of California, San Diego La Jolla, California, USA} Correspondence to: Stig B. Dalsøren (stigbd@cicero.oslo.no)

Received: 11 July 2015 – Published in Atmos. Chem. Phys. Discuss.: 5 November 2015 Revised: 24 February 2016 – Accepted: 26 February 2016 – Published: 9 March 2016

Abstract. Observations at surface sites show an increase in global mean surface methane (CH4) of about 180 parts per billion (ppb) (above 10 %) over the period 1984–2012. Over this period there are large fluctuations in the annual growth rate. In this work, we investigate the atmospheric CH4 evolution over the period 1970–2012 with the Oslo CTM3 global chemical transport model (CTM) in a bottom-up approach. We thoroughly assess data from surface mea-surement sites in international networks and select a sub-set suited for comparisons with the output from the CTM. We compare model results and observations to understand causes for both long-term trends and short-term variations. Employing Oslo CTM3 we are able to reproduce the sea-sonal and year-to-year variations and shifts between years with consecutive growth and stagnation, both at global and regional scales. The overall CH4 trend over the period is reproduced, but for some periods the model fails to repro-duce the strength of the growth. The model overestimates the observed growth after 2006 in all regions. This seems to be explained by an overly strong increase in anthropogenic emissions in Asia, having global impact. Our findings con-firm other studies questioning the timing or strength of the emission changes in Asia in the EDGAR v4.2 emission in-ventory over recent decades. The evolution of CH4 is not only controlled by changes in sources, but also by changes in the chemical loss in the atmosphere and soil uptake. The atmospheric CH4lifetime is an indicator of the CH4loss. In our simulations, the atmospheric CH4lifetime decreases by more than 8 % from 1970 to 2012, a significant reduction of the residence time of this important greenhouse gas. Changes

in CO and NOxemissions, specific humidity, and ozone col-umn drive most of this, and we provide simple prognostic equations for the relations between those and the CH4 life-time. The reduced lifetime results in substantial growth in the chemical CH4loss (relative to its burden) and dampens the CH4growth.

1 Introduction

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near-term warming, due to its strong climate impact on a 20-year time frame (Myhre et al., 2013). Enhanced CH4levels would also increase the ozone levels in surface air (Fiore et al., 2008, 2012; West and Fiore, 2005; Isaksen et al., 2014), and thereby worsen air pollution impacts on vegetation, crops, and human health.

This study seeks to increase our understanding of CH4by providing a detailed analysis on global and regional CH4 evo-lution over the last 40 years. We investigate essential natural and anthropogenic drivers controlling the atmospheric CH4 budget over the period, with a particular focus on the last 15 years. We perform a balanced analysis of both sources and sinks. The sinks depend on the atmospheric oxidation capac-ity, which is determined by complex chemical and meteoro-logical interactions. This study tries to reveal the key chem-ical components and meteorologchem-ical factors affecting recent changes in the oxidation capacity. We compare model stud-ies and observations to understand causes for both long-term trends and short-term variations (year-to-year). We also ad-dress reasons for differences between observed and modelled CH4trends. The methods used are described in Sect. 2. Sec-tion 3 presents the results from our main analysis and discuss them in a broader context related to findings from other stud-ies. Additional sensitivity studies are presented in the Sup-plement. In Sect. 4 we summarize our findings.

2 Methods and approach 2.1 Emissions and sinks 2.1.1 Methane

We used CH4 emissions for anthropogenic sources from EDGAR v4.2 (EC-JRC/PBL, 2011) and biomass-burning and natural sources from Bousquet et al. (2011). In addition we used soil uptake from Bousquet et al. (2011). Combina-tion of two emission inventories (EDGAR v4.2 and Bous-quet et al., 2011) makes it possible to study the impacts of many emission sectors (18 in total, see Table S1 in the Sup-plement for the sectors and specifications of the categories). The EDGAR inventory covers the period 1970–2008 while the Bousquet et al. (2011) data covers the period 1984–2009. Since we study the period 1970–2012 extrapolations were made for the years not covered by the data sets. For all years from 1970 to 1984 we used natural and biomass-burning emissions and soil uptake for 1984. For 2010–2012 we used 2009 data for these sources. For the anthropogenic emissions we extrapolated the change from the period 2007–2008 to the period 2009–2012. The rather simple extrapolations result in additional uncertainties in the model outcome for these years. Figure S1 in the Supplement shows how the emissions are included in the model for the different time periods. The to-tal emissions and emissions from major sectors are shown in Fig. 1. There is a large growth in total emissions from 1970

to 2012. However, shorter periods with declining emissions occur due to large inter-annual variability in natural emis-sions, especially from wetlands which is the largest emission sector. The inter-annual variation in wetland emissions tends to be anti-correlated with the ENSO index (Bousquet et al., 2006; Hodson et al., 2011). Low natural emissions also oc-cur due to lower global temperatures in the years after the Pinatubo eruption. In the 1990s the growth in anthropogenic emissions are small, mainly caused by the economic collapse of the former USSR. From 2000 to 2006 the total emissions are quite stable, and this is caused by decreasing wetland emissions due to dry conditions in the tropics in combination with increasing anthropogenic emissions. From 2006 there is a strong growth in total emissions due to large wetland emissions and a continuing growth of anthropogenic emis-sions. The abrupt increase in 2007 is mainly explained by high wetland emissions caused by high temperatures at high latitudes in the Northern Hemisphere, and wet conditions in the tropics (Bousquet et al., 2011). Enteric fermentation (due to ruminants) is the main anthropogenic emission sector and it grows steadily except for a period in the 1990s. Some other major anthropogenic sectors like gas, solid fuel (mostly coal) and agricultural soils (mostly rice) even decrease over shorter periods but have in common a substantial growth over the last decade. The sum of several smaller anthropogenic emission sectors (industry, residential, waste, some fossil, etc.) are also shown in Fig. 1. This sum termed “other anthropogenic sec-tors” is of the same magnitude as enteric fermentation. The growth is rather stable and moderate with some interruptions: temporary declines occur after the oil crisis in 1973 and the energy crisis in 1979. The growth is also small during the 1990s.

We also explore a possible impact of the recent financial crisis using an alternative extrapolation of anthropogenic emissions for the period 2009–2012. Here, the emissions from petroleum and solid fuel production and distribution were scaled with BP Statistical Review of World Energy (http://www.bp.com/en/global/corporate/energy-economics/ statistical-review-of-world-energy.html) numbers for gas production, oil and coal consumption resulting in a drop in total emissions in 2009 (Fig. 1). However, the evolution from 2010 with this alternative extrapolation is rather similar to that for the standard extrapolation. The EDGAR v4.2 inventory was recently extended to include also the years 2009–2012. In Fig. S2 (Supplement) we compare our extrapolations with the new data and also include a comparison to ECLIPSE v5a emissions that are available for part of our study period (1990–2015, 5-year intervals).

2.1.2 Other components

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S. B. Dalsøren et al.: Atmospheric methane evolution the last 40 years 3101

Figure 1. Emissions used in the model simulations. The grey shaded area is the total CH4emissions (leftyaxis). The total emissions in

the alternative extrapolation accounting for the financial crisis are shown from 2006 and onwards as the grey line with markers. The other coloured lines are the CH4emissions from the main emission sectors (rightyaxis).

emissions we used GFEDv3 (van der Werf et al., 2010) for the period 1997–2012. In the period 1970–1996 we used year-2001 emissions from GFEDv3. 2001 was taken as a proxy for an average year since it has a weak ENSO index for all months (see next section for more discussion on this). The parametrization and inter-annual variation of light-ning NOxemissions are described in Søvde et al. (2012). For other natural emissions we used emission data for 2000 for all years. The oceanic emissions of CO and NMVOCs and soil NOxemissions are from RETRO (Schultz et al., 2008). Sources for natural sulfur emissions are described in Berglen et al. (2004). The emissions from vegetation of CO and NMVOCs are from MEGANv2 (Guenther et al., 2006). Re-cently a new data set (Sindelarova et al., 2014) with MEGAN emissions covering the period 1980–2010 became available. This data set was used in a sensitivity study to investigate whether inter-annual variations in CO and NMVOCs emis-sions from vegetation are important for the CH4evolution. 2.2 Chemical transport model

The emission data over the period 1970–2012 was used as input in the Oslo CTM3 model. A coupled tropospheric and stratospheric version was used. The model was run with 109 chemical active species affecting CH4and atmospheric ox-idation capacity. In addition we added 18 passive fictitious tracers for each of the CH4 emission sectors listed in Ta-ble S1. The traces were continuously emitted and then given an e-folding lifetime of 1 month, undergoing transport but not interacting chemically. The passive tracers were used as a

proxy for the different sector’s contribution to monthly mean surface CH4concentrations. The aim was to reveal key sec-tors and regions behind recent changes in spatial distribution or temporal evolution of CH4.

Oslo CTM3 was described and evaluated by Søvde et al. (2012) and used for studying CH4 lifetime changes in Holmes et al. (2013). Oslo CTM3 is an update of Oslo CTM2 which has been used in a number of previous studies of stratospheric and tropospheric chemistry, including studies on CH4(Dalsøren et al., 2010, 2011; Dalsøren and Isaksen, 2006; Isaksen et al., 2011).

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Figure 2. Global CH4budget in the main Oslo CTM3 simulation over the period 1970–2012: atmospheric burden (leftyaxis); loss:

atmo-spheric chemical destruction+soil uptake (rightyaxis); and total emissions (rightyaxis).

Table 1. Overview of simulations performed with the Oslo CTM3 model.

Simulation name Period Characteristics Difference from main simulation

Main 1970–Oct 2012 Standard emissions described

in Sect. 2.1.1. Meteorology de-scribed in this section.

Fixed methane 1970–Oct 2012 No prescription of methane emissions. Surface methane

levels kept fixed. Monthly mean 1970 levels used re-peatedly for all years

Fixed meteorology 1997–Oct 2012 Year-2001 meteorology

Financial∗ 2009–Oct 2012 Alternative extrapolation of anthropogenic emissions to

account for the financial crisis

Bio∗ 1980–2012 Inter-annual variation in biogenic emissions of

NMVOCs and CO

∗_{Results (and setup) from these simulations are mainly discussed in the Supplement.}

simulation includes the standard CH4emissions described in Sect. 2.1.1. In the “financial” simulation, the period 2009– 2012 was rerun with slightly different emissions evaluating whether the recent financial crisis had any significant impact on CH4levels. With a similar purpose a “bio” simulation was performed accounting for inter-annual variation in emissions of CO and NMVOCs from vegetation. The results from the two sensitivity studies on emissions are discussed in the Sup-plement. In the “fixed methane” simulation, the prescription of methane emissions was turned off and surface CH4 was kept fixed at monthly mean 1970 levels (i.e., boundary con-dition of Dirichlet type instead of Neumann type) to isolate the effect of other components and meteorological factors on CH4 via changes in oxidation capacity. In the “fixed met”

simulation, the period 1997–2012 was repeated using year-2001 meteorology for all years. By comparing this run with the “main” simulation the impact of meteorological variabil-ity could be discerned.

2.3 Observations

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Figure 3. Atmospheric CH4burden and atmospheric chemical loss for the simulation with “fixed meteorology” and the “main” simulation.

Criteria for selection were the length of measurement record (coverage over most of the time periods of interest), access to continuous time series with few gaps, time resolution (at least 2–3 measurement per month), coverage of different re-gions of the Earth, and site characteristics (e.g. elevation, to-pography, and influence of pollution episodes). The last point was evaluated in relation to the resolution of the CTM. From this analysis, 71 observational data sets from 64 stations in the WDCGG database were selected as suited for compar-isons with the CTM results. Comparcompar-isons for some of these stations are shown in Sects. 3.3 and 3.4.

3 The methane evolution and decisive factors over the period 1970–2012

3.1 Global methane budget

Figure 2 shows the evolution of the CH4 budget over the period 1970–2012 for the main simulation. It presents total burden and loss calculated by the forward CTM run and the emissions applied in this simulation. The total burden shown in black is balanced by the emissions (blue) and the loss (red). There is a steady growth in atmospheric CH4 burden from 1970 to the beginning of the 1990s, then a short pe-riod of decline after the Mount Pinatubo volcanic eruption in 1991. After 1994 there is a slight increase in CH4burden towards the millennium. Then the CH4 burden is stable for 5–6 years. After 2006 there is a rapid growth in CH4burden. The evolution of emissions and the modelled CH4burden share many common features (Fig. 2). However, the growth in emissions is about 35 % from 1970 to 2012, while the growth in atmospheric burden is about 15 % (additional bur-den increase after 2012 due to the long response time of CH4, is not accounted for in this number). The CH4 burden

in-creased less than expected solely from the increase in CH4 emissions since a growth in the atmospheric CH4 loss oc-curred over the period. The growth in instantaneous atmo-spheric CH4 loss is almost 25 %. In the period 2001–2006 when emissions were quite stable increasing CH4loss likely contributed to the stagnation of the CH4 growth. Interest-ingly, for 2010–2012, the loss deviates from its steady in-crease over the previous decades. A stabilization of the CH4 loss probably contributed to the continuing increase (2009– 2012) in CH4burden after the high emission years 2007 and 2008. Due to the long response time of CH4this change in the loss pattern might also contribute to future growth in CH4. However, there are additional uncertainties in the model bur-den and loss after 2009 due to the extrapolation of emissions after this year.

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sta-Figure 4. Global mean surface CH4mixing ratio in the main model simulation compared to global mean surface CH4mixing ratio calculated

from the global networks AGAGE (http://agage.eas.gatech.edu/data_archive/global_mean/global_mean_md.txt), NOAA ESRL (http://www. esrl.noaa.gov/gmd/ccgg/mbl/data.php), and WDCGG (http://ds.data.jma.go.jp/gmd/wdcgg/pub/global/globalmean.html).

bilization in CH4 burden (Fig. 3). From the comparison in Fig. 3 it can also be seen that it is meteorological factors and not emissions that cause the large enhancements of CH4 loss in 1998 (El Niño event) and 2010 (warm year on global scale). Such episodes do not show up as immediate pertur-bations of the CH4 burden (Figs. 2 and 3) due to the long response time of atmospheric CH4. Meteorology and other drivers for the modelled evolution of methane loss are dis-cussed in detail in Sects. 3.5–3.6.

3.2 Evolution of global mean surface methane

Figure 4 compares the global mean surface CH4in the main model simulation, to global mean surface CH4 calculated from networks of surface stations. The main picture is dis-cussed in this section while more detailed evaluations of CH4 development on continental scale, trends, and inter-annual variations are made in the following sections. The time evolution of global mean surface CH4 is very simi-lar for the three observational networks shown in Fig. 4 but there are some differences for the absolute methane level. The AGAGE (mountain and coastal sites) and NOAA ESRL (sites in the marine boundary layer) stations are distant from large pollution sources. WDCGG uses curve fitting and data extension methods very similar to those developed by NOAA and many of the same stations (Tsutsumi et al., 2009), but in addition to marine boundary layer sites, WDCGG in-cludes many continental locations strongly influenced by lo-cal sources and sinks (http://www.esrl.noaa.gov/gmd/ccgg/ mbl/mbl.html). The methane emission estimates from Bous-quet et al. (2011) are optimized against atmospheric obser-vations. Since we only use their natural and biomass-burning

emission inventories, we use different anthropogenic emis-sions (from EDGAR), and the OH field in their inverse model is substantially different from our modelled OH, there is no guarantee that our model will match observations.

Our model generally reproduces the different periods of growth and stagnation and the overall observed increase in concentration from 1984 to 2012 of almost 180 ppb is repli-cated. This gives us confidence when evaluating the decisive drivers explaining the variable evolution over time. However, the model fails to reproduce the strength of the growth rate during some eras, for instance the growth since 2006 is over-estimated. Over the whole period the model also underes-timate the observed CH4 level. Even though there are also large uncertainties in total CH4 emission levels (Kirschke et al., 2013; Ciais et al., 2013), we find it more likely that our model overestimates the atmospheric CH4sink. In a re-cent model inter-comparison, the multi-model global mean CH4 lifetime was underestimated by 5–13 % (Naik et al., 2013) compared to observational estimates. Our study shows a similar underestimation of CH4lifetime. Though the multi-model lifetime is within the uncertainty range of observa-tions, it is likely that models tend to overestimate OH abun-dances in the Northern Hemisphere (Naik et al., 2013; Strode et al., 2015; Patra et al., 2014).

3.3 Methane evolution and emission drivers in different regions

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Figure 5. Location of the 18 surface stations used in comparison between measurements and model in this section. Blue: stations in the

Southern Hemisphere; orange: stations in or near North America; green: stations in or near Europe; red: stations in or near Asia.

achieving a good mixing (after 1–2 months) it is converted into the background component. We show how the use of a 1-month e-folding fictitious tracer (total tracer) is valid as a proxy for the inhomogeneous component. The CH4surface emissions act as the sources for the tracer. In the Supple-ment we use the continuity equation for the CH4mole frac-tion (CH4model) as starting point and further arguments to derive the following approximation:

<CH4model>−[<CH4model>]

=B×(<total tracer>−[<total tracer>])

+residual, (1)

where [ ] denotes longitudinal mean along a whole terres-trial parallel and<>denotes annual running mean. We are interested in the inter-annual variation of CH4, so we have carried out annual running means to remove the strong sea-sonal cycle. The subtraction of longitudinal means on each side of Eq. (1) removes the influence of differences in life-times (the mean lifetime of CH4is around 9 years, whereas the mean lifetime of the total tracer is 1 month).Band “resid-ual” are constants (or almost constant) if the prerequisites discussed in the Supplement (Sect. S3, last paragraph) are met. We expect B to be near or equal to 1 and residual to be small. If Band residual were exactly constant, the Pear-son linear correlation coefficient between <CH4model>

−[<CH4model>]and<total tracer>−[<total tracer>] would be exactly equal to 1. The tracer approach then gives valuable information concerning the contribution to CH4 variation from recent regional–local emission or transport changes. We therefore use the correlation coefficient (in-deed, its square,R2: the coefficient of determination obtained

when performing a linear least-square fit between both mag-nitudes in Eq. 1 to determineBand residual) as one criterion when selecting interesting stations for methane trend studies. Only stations whereR2is higher than 0.5 is used. This crite-rion excludes only a small number of the available stations. In addition, we use the general station selection criteria dis-cussed earlier in the manuscript (sufficient coverage in the different world regions, long time series etc., see Sect. 2.3). Figure 5 shows the locations of stations used in Figs. 6–10 for detailed trend analysis and evaluation of model performance. Table 2 shows R2, the constants B and residual, and RMSE from a linear fit of the variables in Eq. (1). All stations except one (reason for exception at the Wendover station is discussed in the Supplement) haveR2above 0.8. Such high coefficients support that the approximation in Eq. (1) is use-ful for these stations. As expected,B is usually larger than 1. The fictitious tracer will underestimate somewhat the in-homogeneous recently emitted CH4, in particular at remote stations, because part of it is removed by the e-folding sink before being smoothed to the characteristic variation length of the background. Mauna Loa is probably the most remote station and located at high altitude. It has the largestB and residual. Alert, Tutuila, Mahe Island, and Key Biscayne are also remote stations that have a highB. As explained below the tracers play a small role in explaining CH4at Cape Grim and Ushuaia, whereBis below 1.

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coeffi-Table 2. Coefficient of determination (R2)between<CH4model>−[<CH4model>]and<total tracer>−[<total tracer>]for stations

shown in Figs. 5–10. Parameters for Eq. (1) and RMSE for a linear fit between<CH4model>−[<CH4model>]and<total tracer> −[<total tracer>].

Station Figure R2between<CH₄model> residual B RMSE

−[<CH4model>]and <total tracer>−[<total tracer>]

Ascension Island 6a 0.80 −3.01 1.21 0.74

Tutuila 6b 0.87 5.08 1.49 0.82

Cape Grim 6c 0.98 −0.15 0.97 0.05

Ushuaia 6d 0.83 −0.27 0.94 0.09

Alert 7a 0.69 −2.16 1.66 0.85

Wendover 7b 0.54 −5.74 0.78 1.07

Key Biscayne 7c 0.95 6.10 1.38 1.40

Mauna Loa 7d 0.87 18.41 1.80 1.27

Zeppelinfjellet 8a 0.91 −1.67 1.13 0.59

Pallas–Sammaltun 8b 0.95 −3.38 1.18 0.75

Mace Head 8c 0.97 −3.28 1.16 0.56

Hegyhatsal 8d 1.00 −2.46 1.15 0.96

Sede Boker 9a 0.83 5.41 1.23 0.97

Ulaan Uul 9b 0.95 1.15 1.10 0.65

Sary Taukum 9c 0.97 −8.27 1.11 0.96

Tae-ahn Peninsula 9d 0.97 0.77 1.07 1.15

Cape Rama 10a 0.92 −9.60 1.24 1.02

Mahe Island 10b 0.85 6.68 1.42 1.22

cients of determination,R2, for most stations (the median is 0.76, andR2is above 0.65 for 15 of 18 stations). The model performance is lower at highly polluted sites due to large gra-dients in concentrations and non-linearity of oxidant chem-istry not fully captured by a global model with coarse reso-lution (approximately 2.8◦×2.8◦). The model also captures the long-term evolution of CH4seen in the observations but overestimates the increase after 2005 at most stations.

The stations in the Southern Hemisphere (Fig. 6) are lo-cated far from the dominating emissions sources, and the CH4concentration is to a large degree determined by trans-port and chemical loss. The high coefficients of determina-tion ranging from 0.92 to 0.95 and reproducdetermina-tion of the sea-sonality and trends indicate that our model is performing ex-cellent with respect to transport and seasonal variation in the chemical loss.

As seen in the mid panels, Ascension Island (Fig. 6a) and Tutuila (Fig. 6b) have negative <total tracer>−[<

total tracer>]. Since these are rather remote stations, their tracer levels are below the longitudinal mean. The modelled CH4 evolution from 1990 to 2005 is well correlated with the development of the natural tracers. However, changes in natural emissions do not seem to explain the periods with large growth before 1990 and for the period 2005–2012. While the model underestimates the growth before 1990 it overestimates the growth in the recent years. The small steady increases in contributions from all anthropogenic sec-tors only has a minor contribution to the modelled CH4

in-crease for these periods. However, since these source trac-ers have an e-folding lifetime of 1 month their evolution is only representative for changes in contribution from re-gional sources. Inter-hemispheric transport occurs on longer timescales; hence, changes in large anthropogenic sources in the Northern Hemisphere most likely also had a signifi-cant contribution as discussed below. At Ascension Island, extra strong influences of regional sources (<CH4model>

−[<CH4model>]change different from zero) are mainly associated with El Niño episodes (1987, 1997–1998, and 2004–2005). In the 1997–1998 period, there are peaks both for the natural tracer and<total tracer>−[<total tracer>]

indicating a rise in nearby natural emissions and/or trans-port from such a source. For 1987 a regional drop in natu-ral emissions has a smaller impact at Ascension compared to the whole latitude band. At Tutuila<total tracer>−[<

total tracer>]decreases over time due to a relatively larger increase in the latitudinal mean anthropogenic tracers (not shown), especially enteric fermentation. This explains why the CH4growth at the site (<CH4model>) is slightly less than the mean latitudinal ([<CH4model>]) growth.

Ushuaia (Fig. 6c) and Cape Grim (Fig. 6d) are the south-ernmost stations. In the mid panels it can be seen that both terms on the right side in Eq. (1) are small (B×(<

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de-S. B. Dalsøren et al.: Atmospheric methane evolution the last 40 years 3107

Figure 6. Evolution of CH4and tracers at stations (a: Ascension Island, b: Tutuila, c: Cape Grim, d: Ushuaia) in the Southern Hemisphere.

Upper panel in each figure: comparison of monthly mean surface CH4in model and observations. The model results are scaled to the

observed mean CH4level over the periods of measurements. Mid panels: variables from Eq. (1).<>denotes annual running mean, [ ]

denotes longitudinal mean. Leftyaxis:<CH₄model>and[<CH₄model>]are scaled down to be initialized to zero in the first year. Rightyaxis:B×(<total tracer>−[<total tracer>]) and residual. Lower panels: Evolution of various emission tracers, see Table S1 in the Supplement for detailed information.

cisive. Distant latitudinal transport is not seen by the tracer term if it takes more than around 2 months. Such trans-port would also result in very similar <CH4model>and

[<CH4model>]since atmospheric species with lifetime of that timescale or longer are quite homogenously distributed over latitudinal bands. Since both the emissions and their

trends are small at high southern latitudes, the distant trans-port likely originates from low latitudes in the Southern Hemisphere or the Northern Hemisphere.

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Figure 7. Evolution of CH₄and tracers at stations (a: Alert, b: Wendover, c: Key Biscayne, d: Mauna Loa) in or near North America. See Fig. 6 caption for further description.

For the latest period, the increase in the model is larger than that observed. The seasonal and year-to-year variations are well represented by the model at all stations (coeffi-cients of determination from 0.73 to 0.82). Key Biscayne (Fig. 7c) and Mauna Loa (Fig. 7d) have relatively large neg-ative <total tracer>−[<total tracer>] which shows that these are background stations and that important emission sources exist at their latitude. The tracer difference is quite small and negative at Alert (Fig. 7b) and since the

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Figure 8. Evolution of CH4and tracers at stations (a: Zeppelinfjellet, b: Pallas–Sammaltun, c: Mace Head, d: Hegyhatsal) in or near Europe.

See Fig. 6 caption for further description.

coal) sector as its contribution increases from 2003 and on-wards. The same occurs for this sector at Alert (Fig. 7a). It corresponds with the start of an increase in US fugitive solid fuel emissions in the applied EDGAR v4.2 inventory. The increase in US coal emissions from 2003 to 2008 is almost 12 % in EDGAR v4.2. An increase of 28 % is found from 2005 to 2010 in the EPA inventory (EPA, 2012). At the high altitude sites Mauna Loa and Wendover (Fig. 7b and d) there are small or large increases in the contribution from all an-thropogenic sectors from the year 2000 and onwards. These

stations are subject to efficient transport from Asia at high altitudes. There are large emission increases after 2000 in eastern Asia in the EDGAR v4.2 inventory (Bergamaschi et al., 2013). Especially coal related emissions in China show a strong increase with a doubling from 2000 to 2008.

At Wendover, Mauna Loa and Key Biscayne

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Figure 9. Evolution of CH4and tracers at stations (a: Sede Boker, b: Ulaan Uul, c: Sary Taukum, d: Tae-ahn Peninsula) near Asian emission

sources. See Fig. 6 caption for further description.

panels); i.e. other locations (for Asian stations, see discus-sion below) at the same latitudes have a larger trend in CH4. There are large fluctuations of tracer transport to Mauna Loa in 1997–1998 and 2010–2011 that strongly impacts

<CH4model>. The observations also show changes in growth and seasonal pattern during these years.

At the Arctic site Zeppelin (Fig. 8a), located on the coast of western Svalbard, there is a small CH4 increase both in model and observations up to 2004. A large part of the CH4

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Figure 10. Evolution of CH4and tracers at stations (a: Cape Rama, b: Mahe Island) in background/outflowing air in or near Asia. See Fig. 6

caption for further description.

by natural source contribution in the model falling from a pe-riod maximum in 2004 to low values in 2005–2006. This is also the case for the sub-Arctic site Pallas (Fig. 8b) located in a region characterized by forest and wetlands. Gas, enteric fermentation and various other small regional anthropogenic sources seems to contribute to the CH4increase at Zeppelin after 2006. The contribution from natural emissions and re-cent regional coal mining peaked in 2007. A quite strong CH4enhancement occurs for 2009–2010 in both the model and observations. The longitudinal mean tracers for individ-ual sectors are almost stable to declining (not shown) while contribution from the<gas>and some other tracers show a small maximum (lower panel Fig. 8a and b). Pallas has a similar pattern. The runs with fixed meteorology suggest en-hanced transport from Russia passing major gas fields and Pallas.

Mace Head (Fig. 8c) is a rural background coastal site in Europe. The result of<total tracer>−[<total tracer>]

is quite large and negative, suggesting important emission sources along the station’s latitude. In the beginning of the 1990s, there is a mismatch between declining model concen-trations and the increase found from the observations. Some of the decrease in the model is due to decreasing contri-butions from solid fuel (mainly coal), enteric fermentation and other regional anthropogenic sources. The station ex-periences unusual meteorological conditions in the ENSO

year 1997, as there are abrupt shifts in concentrations of CH4 and several of the anthropogenic tracers having small year-to-year variations in emissions. Similarly, there seems to be transport of less polluted air masses to the station in 2004 compared to earlier years resulting in lower CH4 con-centration in measurements and model in 2004 and 2005. Several regional sources seem to have small contributions to the modelled and observed CH4 increases from 2006 to 2009. After 2009 we extrapolate emission trends due to lack of emission inventories and this may be the reason why the model doesn’t reproduce the observed levelling off in growth in 2010 and 2011.

The model has larger discrepancies at Hegyhatsal, a semi-polluted site in central Europe (Fig. 8d). Despite seasonal is-sues the model performance is reasonable for the long-term CH4 changes. In years with high contributions from natu-ral sources, the seasonal maxima tend to be too high in the model. It could be that the coarse model resolution results in too much transport from nearby wetlands or that the emission inventory has overly large natural emissions in surround-ing regions.<total tracer>−[<total tracer>]is very large and positive meaning that the station is very sensitive to emissions close upwind. The evolution of<CH4model> therefore deviates strongly from the longitudinal mean[<

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is a period with stable to declining modelled CH4 concentra-tions. This is caused by decreasing central European emis-sions particularly from enteric fermentation and the category “other anthropogenic sectors” together with decreasing or fluctuating natural sources.

In general, the model reproduces the features in the ob-servations over and near Asia quite well (Figs. 9 and 10) with coefficient of determination in the range of 0.24–0.84. For the trends, the overestimation after 2006 is higher here than modelled in other world regions (Figs. 6–8). Gas is the major cause of increases in CH4in Israel (Sede Boker, Fig. 9a). The increase of the<gas>tracer is much larger than for the longitudinal mean [<gas>], suggesting im-portant emission increases from nearby gas fields. Small changes in regional natural emissions and in the category “other anthropogenic sources” (lower panel) are correlated with the modelled year-to-year variations (upper panel). The station in Kazakhstan (Fig. 9c) is downwind of large sources (<total tracer>−[<total tracer>]large and positive), and the modelled CH4increase after 2005 is much larger than for the longitudinal mean. Also at this station, the CH4trend is heavily influenced by gas, although not to the same extent as in Israel. Other regional anthropogenic emission changes also contribute somewhat to the modelled CH4increase over recent years. High natural emissions in 2008–2009 also had an impact. Since we use repetitive year-2009 natural emis-sions for the latter years, it could be that the contribution from this source is too large after 2009. Unfortunately, the modelled CH4 increase cannot be confirmed by measure-ments since data at the station is missing after 2008.

Regional solid fuel emissions (mainly coal) is the main cause of last-decade-modelled CH4increase in eastern con-tinental Asia (Ulaan Uul and Tae-ahn Peninsula, Fig. 9b and d), but gas and other reginal anthropogenic sectors also con-tribute. There is large growth in <CH4model>for Ulaan Uul in 2006–2007 and 2010 mainly due to peaks in the contribution from solid fuel sources, but also other anthro-pogenic sectors have a role in this. Similar pattern appears for Tae-ahn Peninsula in 2009. The first peak at Ulaan Uul is also partly seen in the observations, but the existence of the latest episode and the event at Tae-ahn Peninsula is less clear from the measurements. Our tracer analysis for Minamitori-shima (not shown), a background station affected by outflow from the Asian continent indicates less continental outflow in 2007. For these polluted continental sites the correlation coefficients are lower than for the other stations. The coarse resolution of the model has problems resolving large gradi-ents in concentrations and non-linearity of oxidant chemistry. At Tae-ahn Peninsula <CH4model> starts increasing in 2005, while the increase at Ulaan Uul first starts in 2006. At Ulaan Uul decreasing regional natural emissions over the pe-riod 2000–2005 seems to compensate for the large increase of solid fuel emissions from around 2000.

For Cape Rama in India (Fig. 10a, the observations show signatures of both Northern Hemispheric and

South-ern Hemispheric (NH and SH) air masses (Bhattacharya et al., 2009). Mixed with regional fluxes and varying chemical loss, this results in large seasonal variation. During the sum-mer monsoon, the station is located south of the inter-tropical convergence zone. Air arriving during this period (June to September) represent tropical or SH oceanic air masses and the station is upwind of Mahe Island (Fig. 10b). During the winter monsoon the situation is opposite. There is outflow from the continent affecting both Cape Rama and Mahe Is-land. The ENSO event in 1997 seems to have opposite effects on modelled and observed CH4 variability at Cape Rama. Despite that, the model does a reasonable job in reproduc-ing the measurements. Most regional tracers show stable to upward levels over the period of comparison and likely con-tribute to a small fraction of the modelled CH4 trend. At Mahe Island in the SH (Fig. 10b), the CH4 concentration peaks sharply during NH winter when the station is influ-enced by outflow from continental Asia. The station is there-fore an indicator of inflow to the SH. This feature is well captured by the model. Over the last decade, there is a small and continuous rise in the levels of all anthropogenic tracers at the station. This coincides with large emission increases in Asia, suggesting that the recent development in Asia has some influence on the SH.

3.4 Methane evolution and emission drivers over distinct time periods

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Figure 11. CH4year-to-year variation (ppb) in surface CH4in model (a) compared to the levels of surface CH4estimated from

observa-tions (b) in various latitudinal bands based on the NOAA ESRL network of surface staobserva-tions (Ciais et al., 2013, and data set provided by Edward J. Dlugokencky, personal communication, 2015).

index. Therefore, during the 1987–1988 El Niño, the mete-orology used is less representative than for other years with weaker ENSO. In the two periods of CH4growth before and after 1987–1988, the CH4 increase is strong in the model (Fig. 11a) in the Northern Hemisphere and might be over-estimated. However, it might be that the model is able to bet-ter capture latitudinal gradients, as only a few measurement sites are available to make latitudinal averages for the 1980s. In 1992 and 1993 there is a pause in the CH4growth in the measurements (Fig. 11b) at all latitudes. This pause has been explained as a consequence of the Mount Pinatubo volcanic eruption in 1991 (Dlugokencky et al., 1996; Bekki and Law, 1997; Bânda et al., 2013). The eruption results in an initial increase in the CH4growth rate (less OH) lasting for half a year. This is due to backscattering by volcanic stratospheric aerosols, which reduces the UV radiation to the troposphere. After that, the growth rate due to Pinatubo becomes negative (more OH plus less natural methane emissions are the domi-nating effects) reaching a minimum after 2 years (1993), be-fore levelling off towards zero after 5 years. The main cause of the OH increase is reduction in stratospheric ozone allow-ing more UV radiation to the troposphere. In contrast to the measurements the model shows a stronger decrease in CH4 after the eruption, and the pause in CH4 growth is longer. This might be due the fact that the model does not fully in-clude all factors affecting CH4related to the Mount Pinatubo

eruption. Reduced emissions are implicitly included in the natural CH4emission inventories, but changes in meteorol-ogy (temperature, water vapour, etc.) and volcanic SO2and sulphate aerosols in the stratosphere, are not accounted for in the simulations. In the period 1994–1997 the model struggles to reproduce the latitudinal distribution of growth (Fig. 11). The model seems to have overly large growth in the Tropics probably due to a small but significant growth in wetland and biomass-burning emissions in the period (Fig. 1).

In the next paragraphs, we study whether the model is able to reproduce CH4measurements when we split the time frame into shorter epochs that measured distinct different growth rates. The splits are made within the period 1998– 2009 when our simulations have both inter-annual variation in meteorology and complete emission data (no extrapola-tions made). We have only included observation sites that have measurements available for all months within the given time period, see Sect. 2.3 for details about data selection.

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Figure 12. (Upper panel) Mean year-to-year growth (ppb yr−1) in surface CH4in Oslo CTM3 over the period 1998–2000. The 32 circles

show the observed growth rates over the same period. The stations picked for comparison are based on the criteria described in Sect. 2.3, and only observation sites that have measurements available for all months within the given time is included. (a–f) Mean year-to-year growth ppb yr−1) of emission tracers in the same period. (a) Natural (wetlands+other natural+biomass burning), (b) enteric, (c) agricultural soils,

(d) gas, (e) solid fuel, (f) the sum of all other anthropogenic tracers.

from gas fields in Russia, the Middle East, and in several anthropogenic tracers over India explain why these are the regions in the Northern Hemisphere with largest modelled CH4increase.

Earlier studies find that a low CH4growth rate in the 1990s is mostly caused by lower fugitive fossil fuel emissions from oil and gas industries, mainly due to the collapse of the So-viet Union (Bousquet et al., 2006; Simpson et al., 2012; Dlu-gokencky et al., 2003; Aydin et al., 2011). Another important factor is decreased emissions from rice paddies. Lower emis-sions from agricultural soils last until around the year 2000 in the EDGAR v4.2 inventory (Fig. 1) and are also evident in Fig. 12c. Kai et al. (2011) exclude fossil fuel emissions as the primary cause of the slowdown of CH4growth. According to their isotopic studies, it is more likely long-term reductions in agricultural emissions from rice crops in Asia, or alterna-tively another microbial source in the Northern Hemisphere that is the major factor. Another isotope study (Levin et al., 2012) disagrees and finds that both fossil and microbial emis-sions were quite stable.

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Figure 13. (Upper panel) Mean year-to-year growth (ppb yr−1) in surface CH4in Oslo CTM3 over the period 2001–2006. The 25 circles

show the observed growth rates over the same period. The stations picked for comparison is based on the criteria described in Sect. 2.3, and only observation sites that have measurements available for all months within the given time is included. (a–f) Mean year-to-year growth (ppb yr−1) of emission tracers in the same period. (a) Natural (wetlands+other natural+biomass burning), (b) enteric, (c) agricultural soils,

(d) gas, (e) solid fuel, (f) the sum of all other anthropogenic tracers.

model and measurements (Fig. 11) is very similar for the Southern Hemisphere but there are larger differences for the Northern Hemisphere.

During 2000–2006 the CH4growth levelled off and there was a period with stagnation in global mean growth rate (Fig. 13). The agreement between the zonal averages from the model and the measurement approach is excellent, both with regards to timing and strength of the growth (Figs. 11 and 13). The 2002–2003 anomaly in the Northern Hemi-sphere is captured by the model (Fig. 11) and explained by enhanced emissions from biomass burning in Indonesia and boreal Asia (Bergamaschi et al., 2013; Simpson et al., 2006; van der Werf et al., 2010).

The EDGAR v4.2 inventory applied here and in other stud-ies (e.g. Bergamaschi et al., 2013) show that global anthro-pogenic emissions rise substantially, especially in Asia after the year 2000. This increase in the anthropogenic emissions is compensated by a drop in northern tropical wetland emis-sions associated with years of dry conditions (Bousquet et al., 2006, 2011). Monteil et al. (2011) find that moderate

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Figure 14. (Upper panel) Mean year-to-year growth (ppb yr−1) in surface CH₄in Oslo CTM3 over the period 2007–2009. The 36 circles show the observed growth rates over the same period. The stations picked for comparison are based on the criteria described in Sect. 2.3, and only observation sites that have measurements available for all months within the given time is included. (a–f) Mean year-to-year growth (ppb yr−1) in mole fraction of emission tracers in the same period. (a) Natural (wetlands+other natural+biomass burning), (b) enteric,

(c) agricultural soils, (d) gas, (e) solid fuel, (f) the sum of all other anthropogenic tracers.

Since the observation at the eastern Asian stations close to large anthropogenic sources show smaller changes it is plau-sible that the emission growth is overly strong in the applied EDGAR v4.2 inventory, for this region. However, it is diffi-cult to be conclusive since the few observation sites available are situated in zones with sharp gradients in modelled con-centration changes. The EDGAR v4.2 emissions from the region increase gradually between 2000 and 2008, with a larger growth rate after 2002. Findings from Bergamaschi et al. (2013) question this as they suggest a large increase mostly since 2006.

The period 2007 to 2009 is characterized by strong growth in observed global mean growth rate and even stronger growth in the model (Figs. 11 and 14). The model overes-timation seems to occur almost everywhere. Due to the long lifetime of CH4, strong increase in regional emissions has a global impact. Increases in anthropogenic sources in Asia (e.g. Figs. 9, 14b–f), in particular, natural gas in the Middle East and solid fuel (coal) in eastern Asia have large contribu-tions. The influence from emission increases in these regions

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South America is mainly explained by variations in natural emissions (Fig. 14a).

Other studies (Kirschke et al., 2013; Rigby et al., 2008; Bergamaschi et al., 2013; Bousquet et al., 2011; Dlugo-kencky et al., 2009; Crevoisier et al., 2013; Bruhwiler et al., 2014) attribute the resumed strong growth of observed (Dlugokencky et al., 2009; Rigby et al., 2008; Frankenberg et al., 2011; Sussmann et al., 2012; Crevoisier et al., 2013) global CH4 levels after 2006 to increases in both natural and anthropogenic emissions. However, the share of natural vs. anthropogenic contribution varies in the different stud-ies. The studies agree that abnormally high temperatures at high northern latitudes in 2007 and increased tropical rainfall in 2007 and 2008 resulted in large wetland emissions these years. There is also a likely contribution from forest fires in the autumn of 2006 due to drought in Indonesia aschi et al., 2013; Worden et al., 2013). Top-down (Bergam-aschi et al., 2013; Bousquet et al., 2006, 2011; Kirschke et al., 2013; Bruhwiler et al., 2014) and bottom-up studies (EC-JRC/PBL, 2011; Schwietzke et al., 2014; Höglund-Isaksson, 2012; EPA, 2012) suggest steady moderate to substantial in-creases in anthropogenic emissions in the period 2007–2009. Much of this is due to intensification of oil and shale gas ex-traction in the United States and coal exploitation in China.

Using the EDGAR v4.0 inventory as input to a CTM and observations of CH4and its isotopic composition Monteil et al. (2011) led to the conclusion that a reduction of biomass burning and/or of the growth rate of fossil fuel emissions is needed to explain the observed growth after 2005. The differ-ences between the EDGAR v4.0 and EDGAR v4.2 used in this study are moderate. Other bottom-up inventories (EPA, 2012; Höglund-Isaksson, 2012; Schwietzke et al., 2014) re-port lower increases in anthropogenic emissions, see also comparison with ECLIPSE emission in the Supplement. Us-ing the mean of the EPA and EDGAR v4.2 inventory for an-thropogenic emissions, Kirschke et al. (2013) find that ei-ther is the increase in fossil fuel emissions overestimated by inventories, or the sensitivity of wetland emissions to tem-perature and precipitation is too large in wetland emission models. Schwietzke et al. (2014) and the top-down studies by Bergamaschi et al. (2013) and Bruhwiler et al. (2014) conclude that the EDGAR v4.2 emission inventory overesti-mates the recent emission growth in Asia. This is especially the case for coal mining in China. From our results above, it is plausible that overly high growth of fossil fuel emissions, in particular in Asia, is the reason why the recent CH4growth is higher in our model than for the observations. However, in 2007 and 2008 much of the increase in the model in the Northern Hemisphere is driven by high natural wetland emis-sions. Our natural emissions are from Bousquet et al. (2011) who attributes much of the 2007–2008 increase in total emis-sions to wetlands. According to Bergamaschi et al. (2013) a substantial fraction of the total increase is attributed to an-thropogenic emissions. There is therefore a possibility that we could combine two emission inventories (anthropogenic

from EDGAR v4.2 and natural from Bousquet et al., 2011) that both have overly large growth in the period 2006–2008. Extrapolating anthropogenic emissions that likely have overly strong growth probably explain why the model also overestimates the CH4growth from 2009 to 2012. Mismatch between the spatial distributions of the model and measure-ments (Fig. 11) on regional scales from 2009 to 2012 are ex-pected due to the extrapolation of anthropogenic emissions and use of constant 2009 natural and biomass-burning emis-sions. Of these, especially wetland emissions have large spa-tial and temporal variation from year to year.

3.5 Changes in methane lifetime

The modelled evolution of CH4 is not only decided by changes in sources but also changes in the atmospheric CH4 loss and soil uptake. The CH4lifetime is an indicator of the CH4loss. The lifetime is dependent on the efficiency of soil uptake (Curry, 2009) as well as on concentrations of atmo-spheric chemical components reacting with CH4, including the kinetic rates of the corresponding reactions. It also de-pends on how efficiently the emitted CH4is transported be-tween regions with differences in loss rate. Our prescribed fields for soil uptake (Bousquet et al., 2011) are responsi-ble for about 5 % of the loss and the difference between the year with smallest and largest soil uptake is only 2 %. The main reactant removing CH4chemically in the atmosphere is OH, but there is also a small loss due to reactions with excited atomic oxygen (O1D) and chlorine (Lelieveld et al., 1998; Crutzen, 1991). Due to the limited influence of soil uptake, chlorine, and O1D we will hereafter focus on the role of changes in OH and the kinetic loss rate for this reac-tion. A number of components (CO, NOx, NMVOCs, CH4, SO2, aerosols, meteorological factors, solar radiation) con-trol the atmospheric OH level and the kinetic loss rate (Dal-søren and Isaksen, 2006; Lelieveld et al., 2004; Holmes et al., 2013; Levy, 1971). Due to the extremely high reactivity of OH, measurements on large scale are impossible (Heard and Pilling, 2003). Forward models have been employed to cal-culate the OH evolution over time on global scale. Another alternative is inverse models in combination with observa-tions of14CO , CH3CCl3or other long-lived species reacting with OH. This section discusses the modelled evolution of CH4lifetime in this study and compares it to findings from other relevant studies on CH4lifetime and OH change. In the section thereafter we try to identify the key drivers behind the modelled changes in CH4lifetime.

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Figure 15. Evolution of yearly global average atmospheric instantaneous CH₄ lifetime in the main and fixed methane simulations (left

yaxis). Evolution of yearly global average atmospheric OH concentration in the main simulation (rightyaxis) using the reaction rate with CH4as averaging kernel.

with methane is used as an averaging kernel to examine the OH change relevant for changes in methane lifetime. There is a very strong anti-correlation between the evolution of OH and methane lifetime suggesting causality. This is especially the case for the period 1970–1997 run without inter-annual variation in meteorology resulting in a static CH4+OH re-action rate (k) for these years. The lifetimes in the fixed CH4 run (red line) and the main CH4run (blue line) are highly cor-related. This is another way of illustrating that OH (k×OH), and not the CH4burden itself, is driving the long-term evo-lution and year-to-year variations of CH4lifetime. However, some influence from CH4fluctuations is evident in a few of the years studied (mainly in the 1980s), with large variations in CH4emissions (Fig. 1). CH4itself is important for its own lifetime length (blue line well above red line), due to the de-crease in the OH concentration produced by the reaction with the CH4.

Other forward models also suggest a similar decrease in CH4lifetime due to an increase in global OH concentrations the recent decades (Karlsdóttir and Isaksen, 2000; Dentener et al., 2003; Wang et al., 2004; Dalsøren and Isaksen, 2006; Fiore et al., 2006; John et al., 2012; Holmes et al., 2013; Naik et al., 2013). However, some of these studies focus on the effect of certain factors (emissions or meteorology) and do not cover changes in all central physical and chem-ical parameters affecting CH4 lifetime. Using observations of CH4 and its isotopic composition, Monteil et al. (2011) find that moderate (<5 % per decade) increases in global OH over the period 1980–2006 are needed to explain the ob-served slowdown in the growth rate of atmospheric CH4at the end of that period. In contrast, large increases in OH in

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Figure 16. Development in atmospheric CH4lifetime and key parameters known to influence CH4lifetime. All variables values are relative

to 1970. (To make it apparent in the figure, temperature variations are relative to the Celsius scale).

and the increase of stratospheric chlorine (larger loss through reaction with Cl).

It is evident from the above discussion that there are uncer-tainties related to all methods (models, CH3CCl3, and14CO) and missing consensus on OH trends. To increase under-standing and facilitate discussion, it is important not to stop by a derived number for change in OH or methane lifetime, but investigate the major drivers for the changes. The next section address drivers in this model study.

3.6 Major drivers for changes in the methane lifetime Figure 16 shows the evolution of main factors known to de-termine atmospheric CH4 lifetime. The factors chosen are based on the study by Dalsøren and Isaksen (2006) and Holmes et al. (2013).

Using the NOx/CO emission ratio and linear regression analysis (Dalsøren and Isaksen, 2006) found a simple equa-tion describing the evoluequa-tion of OH resulting from emission changes in the period 1990–2001. In general, CO emission increases lead to an overall reduction in current global av-eraged OH levels. An increase in NOx emissions increases global OH as long as it takes place outside highly polluted regions. In this study the general picture is that the NOx/CO emission ratio increases over the 1970–2012 period (Fig. 16). Despite the general increase, periods of declining ratio can be seen both after the oil crisis in 1973 and the energy crisis in 1979. This occurs since NOxemissions are more affected than CO emissions. After 1997 when we include year-to-year variation in emissions from vegetation fires the NOx/CO emission ratio is more variable. Large drops in ratio can be

seen in years with high incidences of fires resulting in large CO emissions. This is typical for ENSO episodes (1997– 1998) and warm years (2010). Agreement with observed CO trends (see comparison in Supplement Sect. S5) indicates that the modelled changes of CO and OH, and applied CO emissions are internally consistent.

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Figure 17. CH₄lifetime evolution 1970–1996. Comparison of the main model simulation (blue line) with CH₄lifetime from the simple model (red line) obtained from multiple linear regression.

the parameters in Fig. 16. Such equations could be very use-ful for fast prediction of future development of CH4lifetime and CH4burden. Since we study different time periods than Dalsøren and Isaksen (2006) and Holmes et al. (2013), and both emissions and meteorology are perturbed in our simu-lations, it is not obvious that simplified equations would be statistically valid.

Figure 17 shows the results of multiple linear regression analysis performed to describe the CH4lifetime over the pe-riod 1970 to 1996. For this pepe-riod, fixed year-to-year mete-orology was used in the main model simulation. This means that parameters like lightning NOx, temperature, and specific humidity (Fig. 16) can be kept out of the regression analy-sis. The equation best reproducing (R2=0.99) the lifetime evolution from the main run (Fig. 17) and having statistical significant linear relations between its parameters and CH4 lifetime is the following:

CH4lifetime(yr)=11.9−21.4×(NOx/CO)emissions. This confirms the analysis from previous sections suggesting that CH4itself has small influence on the variation in CH4 lifetime during this period. The same seems to be the case for variations in ozone column. A similar simple equation was found by Dalsøren and Isaksen (2006). This suggests that near-future variation of CH4lifetime due to changes in emis-sions can be predicted solely by looking at the ratio of NOx to CO emissions. However, it should be noted that the region of emission change is important (Berntsen et al., 2006). This is especially the case for NOxemissions due to the short at-mospheric NOxlifetime. For instance, changes in NOx

emis-sions at low latitudes with moderate pollution levels (OH re-sponse is non-linear) would have profound impacts on CH4 lifetime due to the temperature dependency of the reaction between CH4and OH.

The blue line in Fig. 18 shows the lifetime over the pe-riod 1997–2012 as predicted by the main model run. The red line shows the best fit from a simple parametric model. Be-cause the main CTM run for this period include year-to-year variation in meteorology, the simple regression model need more parameters to reproduce the evolution. Still, a simpli-fied equation (R2=0.99) is statistically valid, predicting the CH4lifetime by a linear combination of the parameters spe-cific humidity (q), NOx/CO emission ratio (NOx/CO)e, lightning NOxemissions (LNOx)e, and O3column:

CH4lifetime(yr)=0.07×O3column−4.80×(NOx/CO)e

−0.04×q−1.21×(LNOx)e.

It should be noted that specific humidity and temperature have almost identical year-to-year variation, and it is there-fore not given which of these parameters should be used.

4 Summary and conclusions

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Figure 18. CH4lifetime evolution 1997–2012. Comparison of main model simulation (blue line) with CH4lifetime from simple model (red

line) obtained from multiple linear regression.

2013; Nisbet et al., 2014). As the quality and detail level of models, input data, and measurements progress, the chances of understanding more pieces in the big puzzle increase. This study is an effort in such a perspective.

In our bottom-up approach, a global chemical transport model (CTM) was used to study the evolution of atmospheric CH4over the period 1970–2012. The study includes a thor-ough comparison with CH4measurements from surface sta-tions covering all regions of the globe. The seasonal varia-tions are reproduced at most stavaria-tions. The model also repro-duces much the observed evolution of CH4 on both inter-annual and decadal time scales. Variations in wetland emis-sions are the major drivers for year-to-year variation of CH4. Regarding trends, the causes are much debated, as discussed in the previous sections. Consensus is neither reached on the relative contribution from individual emission sectors, nor on the share of natural vs. anthropogenic sources. The fact that our simulations capture much of the observed regional changes indicates that our transport and chemistry schemes perform well and that applied emission inventories are rea-sonable with regard to temporal, spatial, sectoral, and nat-ural vs. anthropogenic distribution of emissions. However, there are some larger discrepancies in model performance questioning the accuracy of the CH4 emission data in cer-tain regions and periods. Potential flaws in emission data are pinpointed for recent years when our model simulations are more complete with regard to input data (e.g. emissions, variable meteorology, etc.) and there are more measurements available for comparison. After a period of stable CH4levels from 2000 to 2006, observations show increasing levels from

2006 in both hemispheres. From 2006, the model overesti-mates the growth in all regions, in particular in Asia. Large emission growth in Asia influences the CH4trends in most world regions. Our findings support other studies, suggesting that the recent growth in Asian anthropogenic emissions is too high in the EDGAR v4.2 inventory. Based on our model results and the comparison between ECLIPSE and EDGAR v4.2 emissions in the Supplement (Sect. S2) we also ques-tion the Asian emission trends in the 1990s and beginning of the 2000s in the EDGAR v4.2 inventory, although the lim-ited number of measurement sites in Asia makes it difficult to validate this.

The modelled evolution of CH4 is also dependent on changes in the atmospheric CH4loss. The CH4lifetime is an indicator of the CH4loss. In our simulations, the CH4 life-time decreases by more than 8 % from 1970 to 2012. The rea-son for the large change is increased atmospheric oxidation capacity. Such changes are in theory driven by complex inter-actions between a number of chemical components and me-teorological factors. However, our analysis reveals that key factors for the development are changes in specific humidity, NOx/CO emission ratio, lightning NOx emissions, and to-tal ozone column. It is statistically valid to predict the CH4 lifetime by a combination of these parameters in a simple equation. The calculated change in CH4lifetime is within the range reported by most other bottom-up model studies. How-ever, findings from these studies do not fully agree with top-down approaches using observations of CH3CCl3or14CO.

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higher. Increasing CH4 loss also likely contributed to the stagnation of CH4 growth in the period 2001–2006. Inter-estingly, over the last few years, the loss deviates from its steady increase over the previous decades. Much of this de-viation seems to be caused by variation in meteorology. Our simulations reveal that accounting for variation in meteorol-ogy has a strong effect on the atmospheric CH4loss. This in turn affects both inter-annual and long-term changes in CH4 burden. A stabilization of the CH4loss, mainly due to me-teorological variability, likely contributed to a continuing in-crease (2009–2012) in CH4burden after high emission years in 2007 and 2008. Due to the long response time of CH4this could also contribute to future CH4growth. However, there are extra uncertainties in the model results after 2009 due to lack of comprehensive emission inventories. A new inven-tory or update of existing ones with sector–vice separation of emission for recent years (2009–2015) would be a very valuable piece for model studies trying to close the gaps in the CH4puzzle. It will also provide important fundament for more accurate predictions of future CH4 levels and various mitigation strategies.

The Supplement related to this article is available online at doi:10.5194/acp-16-3099-2016-supplement.

Acknowledgements. This work was funded by the Norwegian

Research Council project GAME (Causes and effects of Global and Arctic changes in the Methane budget), grant no. 207587, under the program NORKLIMA, and the EU project ACCESS (Arctic Climate Change Economy and Society). ACCESS received funding from the European Union under grant agreement no. 265863 within the Ocean of Tomorrow call of the European Commission Seventh Framework Programme. We are grateful to Phillipe Bousquet for providing and sharing data sets on methane emissions. The work and conclusions of the paper could not been achieved without globally distributed observational data and we acknowledge all data providers, and the great efforts of AGAGE, NOAA ESRL, and The World Data Centre for Greenhouse Gases (WDCGG) under the GAW programme for making data public and available. Specific thanks go to Nina Paramonova, Hsiang J. Wang, Simon O’Doherty, Yasunori Tohjima, Edward J. Dlugokencky, who are the PIs of the observation data shown in Figs. 6–10 and 12–14. We also thank Edward J. Dlugokencky for sharing the observational based data set for Fig. 11, and WDCGG and Paul Novelli for sharing CO data sets used in Fig. S5 of the supplement.

Edited by: P. Jöckel

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